Comparison with state-of-the-art.
Table REF  shows that AugBN gives a huge 17% improvement on CIFAR-10-C compared to just using the source model directly, and also compares favourably with the state-of-the-art approaches. This justifies the effectiveness of the proposed framework for the SITA setting. The performance for every corruption is shown in Figure REF  for CIFAR-10-C.
AugBN outperforms the baselines for majority of the corruption types. ImageNet-C is a much harder dataset to do adaptation using just a single instance, and the methods which do not incorporate source statistics (TENT [1]} and PTN [2]}) suffer a huge loss in performance as their normalisation statistics are highly incompatible with the source model.
Meanwhile, BN [3]} and AugBN which add a prior weight towards source statistics can handle adaptation using a single image better (Table REF ).
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